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Proceedings Paper

Fast iterative censoring CFAR algorithm for ship detection from SAR images
Author(s): Dandan Gu; Hui Yue; Yuan Zhang; Pengcheng Gao
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Paper Abstract

Ship detection is one of the essential techniques for ship recognition from synthetic aperture radar (SAR) images. This paper presents a fast iterative detection procedure to eliminate the influence of target returns on the estimation of local sea clutter distributions for constant false alarm rate (CFAR) detectors. A fast block detector is first employed to extract potential target sub-images; and then, an iterative censoring CFAR algorithm is used to detect ship candidates from each target blocks adaptively and efficiently, where parallel detection is available, and statistical parameters of G0 distribution fitting local sea clutter well can be quickly estimated based on an integral image operator. Experimental results of TerraSAR-X images demonstrate the effectiveness of the proposed technique.

Paper Details

Date Published: 15 November 2017
PDF: 6 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106053E (15 November 2017); doi: 10.1117/12.2295682
Show Author Affiliations
Dandan Gu, Science and Technology on Electromagnetic Scattering Lab. (China)
Hui Yue, Science and Technology on Electromagnetic Scattering Lab. (China)
Yuan Zhang, Science and Technology on Electromagnetic Scattering Lab. (China)
Pengcheng Gao, Science and Technology on Electromagnetic Scattering Lab. (China)


Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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